Implementing Machine Learning Models for Prediction of Gender-Affirming Mastectomy Complications: Estimating Performance and Accuracy.

ABSTRACT:

Background

Machine learning (ML) gained recent popularity because of its usefulness and applicability in medicine. Surgical specialties utilize ML for patient selection, optimization, and prediction of outcomes.

Objectives

The aim of the authors of this study is to develop, validate, and compare ML algorithms for prediction of gender-affirming mastectomy complications.

Methods

Analysis of gender-affirming mastectomies performed by the senior author was performed retrospectively. Six ML algorithms were trained and optimized based on a portion of the data and tested on the remainder. Models were compared in accuracy of prediction, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Receiver operating characteristic (ROC) curve

SUBMITTER: Skorochod R 

PROVIDER: S-EPMC12683286 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature